“Semantic Technologies for Smart Services”

KIT – The Research University in the Helmholtz Association www.kit.edu
Institute of Applied Informatics and Formal Description Methods (AIFB), Karlsruhe Service Research Institute (KSRI),
FZI Research Center for Information Technology
Semantic Technologies for Smart Services
Rudi Studer & Maria Maleshkova
Cognitive Systems Institute Speaker Series, 15 December 2016

Institute of Applied Informatics and Formal
Description Methods (AIFB)
2
“Semantic Karlsruhe”
Industrie 4.0
Medicine &
eHealth
Digital Shift
Big Data &
Data Analytics
SEMANTIC TECHNOLOGIES
Semantic
Data Management
Complex Event
Processing
Data / Text Mining
Smart
Services
Basic
Research
Applied
Research
Transfer
Prof. Dr. Rudi Studer | Cognitive Systems Institute Speaker Series

3
WEB SCIENCE AND
KNOWLEDGE MANAGEMENT
Institute of Applied
Informatics and Formal
Description Methods

4
Karlsruhe Service Research Institute – an „industry-on-
campus“ model with focus on interdisciplinary research
  

 

Prof. Dr. Christof
Weinhardt
Information & Market
Engineering
Prof. Dr. Gerhard
Satzger
Digital Service
Innovation
Prof. Dr. Stefan Nickel
Discrete Optimization
& Logistics
Prof. Dr. Wolf Fichtner
Energy Economics
Prof. Dr. Alexander
Mädche
Information Systems &
Service Design
Prof. Dr. Rudi Studer
Knowledge Management
Prof. Dr. York Sure-
Vetter
Prof. Dr. Kai Furmans
Value Stream Services
Dr. Markus Bauer

5
Service Research investigates complex service systems where
economic value is created jointly by multiple independent parties, acting
together efficiently through the systematic use of information and
communication technologies…
…from different perspectives and in different domains
... and others
Healthcare
Services
Crowd and
Participation
Services
(e)-Mobility
Smart
Services,
Industry 4.0
and IoT
Research Focus:
Intelligent Services for Real-world Networks

6
• Motivation
• Why Smart Services via Semantic Technologies?
• Use Case 1 - Building Agile Systems
• Use Case 2 – Smart Services for Predictive
Maintenance
• Summary and Conclusions

7
Market
Influence
Technology
Development
Today’s Driving Forces
Shorter innovation cycles
Need for continuous adaptation
Near real-time analyses
Involvement of the customer not only with
the finished product/service but during the
complete development cycle
Ubiquitous access
Social and community Web
Heterogeneous big data
Distributed component-based
solutions

8
Internet of Things (IoT) Challenges
We expect one hundred billion IoT devices to be deployed
within the next ten years
BUT the IoT is currently facing a lot of problems
Product silos that do not interoperate with each other
Many approaches and incompatible platforms
No network effect
Heterogeneity in terms of
Data
Devices and interfaces
Data volumes and number of sources explode
We expect one hundred billion IoT devices to be deployed
within the next ten years
BUT the IoT is currently facing a lot of problems
Product silos that do not interoperate with each other
Many approaches and incompatible platforms
No network effect
Heterogeneity in terms of
Data
Devices and interfaces
Data volumes and number of sources explode
see: http://www.w3.org/2015/05/wot-framework.pdf

9
The Web as the Solution
Source: http://www.w3.org/2015/05/wot-framework.pdf

10
Semantic Technologies for Smart Services
Data Integration – combining data from multiple sources enables
new applications and insights
More and more data available on the Web is published conforming to
Semantic Web standards
Linking Open Data (LOD) initiative
Semantic Web technologies are beneficial for data exchange, integration
and search
Decentralised Architectures – no central controller or repository
Overcoming device heterogeneity – common model for devices
(functional and non-functional properties)
Overcoming interface heterogeneity – standard Web Technologies +
Linked Data
Adaptation – adjusting services, products, things according to context
and current needs
Intelligent Programmable Interfaces
Embedding intelligence into the service interface (e.g. rules)

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Semantic Technologies
Semantic Web technologies,
standardised by the W3C, are
mature:
RDF recommendation in 1999,
update in 2004
RDFa (RDF in HTML) note in 2008
RDFS recommendation in 2004
SPARQL recommendation in 2008
OWL recommendation in 2004,
update in 2009
Linked Data is a subset of the
Semantic Web stack

12
Use Cases
1. Building Agile Systems
Fast integration of data and programmable interfaces based on semantic
technologies
2. Smart Services for Predictive Maintenance
Semantics for integrating sensor data, background knowledge and
decision rules
Recognizing maintenance
needs before they occur
Semantic
description
Semantic
description
Semantic
description

13
BUILDING AGILE SYSTEMS
Semantics for integrating data and programmable interfaces

14
Today‘s Web is about Dynamic Data
Data is often dynamically created as a result of some calculation
carried out over input data (e.g., weather information)
Data can change frequently (e.g., moving objects)
APIs are used to trigger functionalities in the Web and the real world
and provide access to dynamic and static data sources
An important role plays
Representational State Transfer
(REST)
Architectural style for client–
server interaction
Compatible with Web architecture
http://programmableweb.com
8816 APIs
Over 16,400 APIs and 7,800 mashups

15
Linked Data Principles
1. Use URIs to name things; not only documents, but also people, locations,
concepts, etc.
http://dbpedia.org/resource/Johannes_Gutenberg
2. To enable agents (human users and machine agents alike) to look up those
names, use HTTP URIs
http://dbpedia.org/page/Printing_press
3. When someone looks up a URI we provide useful information; with 'useful' in
the strict sense we usually mean structured data in RDF
http://dbpedia.org/page/Printing_press
dct:subject dbc:Johannes_Gutenberg.
4. Include links to other URIs allowing agents (machines and humans) to
discover more things
<http://dbpedia.org/page/Printing_press> rdfs:seeAlso
<http://dbpedia.org/page/Letterpress_printing> .
http://www.w3.org/DesignIssues/LinkedData

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Linking Open Data Cloud
Linking Open Data cloud diagram 2014, by Max Schmachtenberg, Christian Bizer, Anja Jentzsch and Richard Cyganiak. http://lod-cloud.net/

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Motivation for Combining Semantics and
Services
Increased value comes from combinations of services and
APIs
But a lot of manual effort is required for this compositions (glue code)
Structured service/API descriptions ease the composition process considerably
Semantic descriptions allow for execution of several tasks automatically
(e.g., data matching, discovery, ranking)
Manually drafted
glue code
Semantic
description
Semantic
description
Semantic
description

18
Motivation for Combining Semantics and
Services
Increased value comes from combinations of services and
APIs
But a lot of manual effort is required for this compositions (glue code)
Structured service/API descriptions ease the composition process considerably
Semantic descriptions allow for execution of several tasks automatically
(e.g., data matching, discovery, ranking)
Manually drafted
glue code
Semantic
description
Semantic
description
Semantic
description

19
Creating Linked Services
Functionality attainable via the Web by combining:
RESTful services (respecting Web architecture)
resource-oriented
manipulated with HTTP verbs
GET, PUT (, PATCH), POST, DELETE
Negotiate representations
Linked data
Uniform use of URIs
Use of RDF and SPARQL
= Linked Services

20
Facilitate Data Integration
Linked Service
Combines data (MashUp)
build on top
Application
that consumes one
Linked Service
Bad solution

21
Linked Service
Combines data (MashUp)
build on top
Application
that consumes one
Linked Service
Bad solution

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Linked
Service
Application
(integrates data and
functionalities from several
Linked Services, e.g. via Linked
Data-Fu)
Good solution
Linked
Service
Linked
Service
http://linked-data-fu.github.io/

23
SMART SERVICES FOR
PREDICTIVE MAINTENANCE
Semantics for integrating sensor data, background knowledge and
decision rules

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Cognition Framework
Perception Reaction
Background
Knowledge
Interpretation and
Analysis

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The Cognition Framework for Predictive
Maintenance
Input data in terms of
- Sensor data
- Personal observations
- Alarms and errors
Background knowledge
- Log files
- Previous similar problems
and solutions
- Guidelines
- Manuals
- Detail about the machines
Interpretation and Analysis
- Data integration to enable
analysis
- Similarity analysis with
previous problems
- Heuristics encoded as rules
Reaction
- Automated solution
recommendation vs.
- Providing solution support

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Problem Breakdown
1. Smart Services for Problem Recognition
Recognizing what the current problem is based on previous problems
Combination with heuristics
2. Smart Services for preparing Solution Containers
Providing summary of the problem, difficulty, time estimate
Links to relevant manuals, links to required parts
Required expertise, contacts of people with relevant qualifications
?
http://www.aifb.kit.edu/web/STEP/en

27
Problem Breakdown
2. Smart Services for preparing Solution Containers (continued)
Dealing with multilingual and multimodal sources
Identifying related articles across different languages and media types
Possible use – the solution might be available in another language;
images and videos can be used to identify the problem, support the solution
3. Smart Services for Interactive
Problem Solving
Guiding the user towards the solution
Recommending the next possible step
http://xlime.eu/

28
Problem Breakdown
4. Smart Services for Route Planning for the technician
Supporting the dispatcher in planning the routes
Supporting the technician during the trips
Solution based on Use Case 1: Building Agile Systems
Creating Linked Services for the interfaces
Rules for defining the composition and interaction
Automated execution with Linked DataFu
Prototype system for data / service integration and execution
Proximity service
Street View
Maintenance route-planning

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Summary and Outlook
Market trends and technology developments pave the way for
developing new products and services, which are more flexible and
adapted to the customer needs
We need technology solutions to achieve more automation and
adaptability –– putting the ‘Smartness’ into services
Providing means for agile system development
Providing means for self-adaptivity
We can use Semantic Technologies for Smart Services to support:
The rapid development of mashups and applications
To realize Industry 4.0 / IoT solutions

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Relevant Publications
S. Stadtmüller, S. Speiser, A. Harth, R. Studer
Data-Fu: A Language and an Interpreter for Interaction with Read/Write Linked Data.
Proceedings of the 22nd International Conference on World Wide Web, pp. 1225-1236, Rio
de Janeiro, 2013.
A. Harth, C. Knoblock, S. Stadtmüller, R. Studer, und P. Szekely. On-the-fly Integration of
Static and Dynamic Sources. Proceedings of the ISWC Workshop
on Consuming Linked Data. 2013: CEUR-WS.
M. Maleshkova, P. Philipp, Y. Sure-Vetter, R. Studer. Smart Web Services (SmartWS) –
The Future of Services on the Web. IPSI BgD Transactions on Advanced Research
(TAR), 12 (1), pp. 15-26, January, 2016.
T. Weller, M. Maleshkova, K. März, L. Maier-Hein. A RESTful Approach for Developing
Medical Decision Support Systems. The Semantic Web: ESWC 2015 Satellite
Events, pp. 376-384, Springer, 9341.
T. Weller, M. Maleshkova. Cognitive Process - An Open-Source Tool to Capture
Processes according to the Linked Data Principles. The Semantic Web: ESWC 2016
Satellite Events, Springer.
L. Zhang, A. Rettinger, J. Zhang. A Knowledge Base Approach to Cross-Lingual
Keyword Query Interpretation. The 15th International Semantic Web Conference
(ISWC'16), Springer, Oktober, 2016

“Semantic Technologies for Smart Services”

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